knitr::opts_chunk$set(echo = TRUE)
cmdstanr::set_cmdstan_path(path = "C:/Users/kueng/.cmdstan/cmdstan-2.35.0")
library(tidyverse)
library(R.utils)
library(wbCorr)
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)
library(see)
library(beepr)
library(DHARMa)
library(digest)
source(file.path('Functions', 'ReportModels.R'))
source(file.path('Functions', 'PrettyTables.R'))
source(file.path('Functions', 'ReportMeasures.R'))
source(file.path('Functions', 'PrepareData.R'))
report_function_hash <- digest::digest(summarize_brms)## [1] 1116
# Set options for analysis
use_mi = FALSE
shutdown = FALSE
report_ordinal = FALSE
report_hurdle = TRUE
do_priorsense = FALSE
options(
dplyr.print_max = 100,
brms.backend = 'cmdstan',
brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
brms.file_refit = 'on_change',
#brms.file_refit = 'always',
error = function() {
beepr::beep(sound = 5)
if (shutdown) {
system("shutdown /s /t 180")
quit(save = "no", status = 1)
}
}
, es.use_symbols = TRUE
)
####################### Model parameters #######################
iterations = 12000 # 10'000 per chain to achieve 40'000
warmup = 2000
# NO AR!!!
#corstr = 'ar'
#corstr = 'cosy_couple'
#corstr = 'cosy_couple:user'
################################################################
suffix = as.character(iterations)df <- openxlsx::read.xlsx(file.path('long.xlsx'))
df_original <- df
df_double <- prepare_data(df, recode_pushing = TRUE, use_mi = use_mi)[[1]]Constructing scales Re-coding pusing reshaping data (4field) centering data within and between
# For indistinguishable Dyads
model_rows_fixed <- c(
'Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw',
'day',
'weartime_self_cw',
# '-- BETWEEN PERSON MAIN EFFECTS',
'persuasion_self_cb',
'persuasion_partner_cb',
'pressure_self_cb',
'pressure_partner_cb',
'pushing_self_cb',
'pushing_partner_cb',
'weartime_self_cb'
)
model_rows_fixed_ordinal <- c(
model_rows_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rows_fixed[2:length(model_rows_fixed)]
)
model_rows_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(persuasion_self_cw)',
'sd(persuasion_partner_cw)',
'sd(pressure_self_cw)',
'sd(pressure_partner_cw)',
'sd(pushing_self_cw)',
'sd(pushing_partner_cw)',
# '-- CORRELATION STRUCTURE -- ',
'sigma'
)
model_rows_random_ordinal <- c(model_rows_random,'disc')# For indistinguishable Dyads
model_rownames_fixed <- c(
"Intercept",
# "-- WITHIN PERSON MAIN EFFECTS --",
"Daily persuasion experienced",
"Daily persuasion utilized (partner's view)", # OR partner received
"Daily pressure experienced",
"Daily pressure utilized (partner's view)",
"Daily pushing experienced",
"Daily pushing utilized (partner's view)",
"Day",
"Daily weartime",
# "-- BETWEEN PERSON MAIN EFFECTS",
"Mean persuasion experienced",
"Mean persuasion utilized (partner's view)",
"Mean pressure experienced",
"Mean pressure utilized (partner's view)",
"Mean pushing experienced",
"Mean pushing utilized (partner's view)",
"Mean weartime"
)
model_rownames_fixed_ordinal <- c(
model_rownames_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rownames_fixed[2:length(model_rownames_fixed)]
)
model_rownames_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
"sd(Daily persuasion experienced)",
"sd(Daily persuasion utilized (partner's view))", # OR partner received
"sd(Daily pressure experienced)",
"sd(Daily pressure utilized (partner's view))",
"sd(Daily pushing experienced)",
"sd(Daily pushing utilized (partner's view))",
# '-- CORRELATION STRUCTURE -- ',
'sigma'
)
model_rownames_random_ordinal <- c(model_rownames_random,'disc')rows_to_pack <- list(
"Within-Person Effects" = c(2,9),
"Between-Person Effects" = c(10,16),
"Random Effects" = c(17, 23),
"Additional Parameters" = c(24,24)
)
rows_to_pack_ordinal <- list(
"Intercepts" = c(1,6),
"Within-Person Effects" = c(2+5,9+5),
"Between-Person Effects" = c(10+5,16+5),
"Random Effects" = c(17+5, 23+5),
"Additional Parameters" = c(24+5,24+6)
)HURDLE MODELS
# For indistinguishable Dyads
model_rows_fixed_hu <- c(
'Intercept',
'hu_Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw',
'day',
'weartime_self_cw',
# '-- BETWEEN PERSON MAIN EFFECTS',
'persuasion_self_cb',
'persuasion_partner_cb',
'pressure_self_cb',
'pressure_partner_cb',
'pushing_self_cb',
'pushing_partner_cb',
'weartime_self_cb',
# HURDLE MODEL
# '-- WITHIN PERSON MAIN EFFECTS --',
'hu_persuasion_self_cw',
'hu_persuasion_partner_cw',
'hu_pressure_self_cw',
'hu_pressure_partner_cw',
'hu_pushing_self_cw',
'hu_pushing_partner_cw',
'hu_day',
'hu_weartime_self_cw',
# '-- BETWEEN PERSON MAIN EFFECTS',
'hu_persuasion_self_cb',
'hu_persuasion_partner_cb',
'hu_pressure_self_cb',
'hu_pressure_partner_cb',
'hu_pushing_self_cb',
'hu_pushing_partner_cb',
'hu_weartime_self_cb'
)
model_rows_fixed_hu_ordinal <- c(
model_rows_fixed_hu[1:2],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rows_fixed_hu[3:length(model_rows_fixed_hu)]
)
model_rows_random_hu <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(hu_Intercept)',
'sd(persuasion_self_cw)',
'sd(persuasion_partner_cw)',
'sd(pressure_self_cw)',
'sd(pressure_partner_cw)',
'sd(pushing_self_cw)',
'sd(pushing_partner_cw)',
# HURDLE
'sd(hu_persuasion_self_cw)',
'sd(hu_persuasion_partner_cw)',
'sd(hu_pressure_self_cw)',
'sd(hu_pressure_partner_cw)',
'sd(hu_pushing_self_cw)',
'sd(hu_pushing_partner_cw)',
# '-- CORRELATION STRUCTURE -- ',
'sigma'
)
model_rows_random_hu_ordinal <- c(model_rows_random_hu,'disc')# For indistinguishable Dyads
model_rownames_fixed_hu <- c(
"Intercept",
"Hurdle Intercept",
# "-- WITHIN PERSON MAIN EFFECTS --",
"Daily persuasion experienced",
"Daily persuasion utilized (partner's view)", # OR partner received
"Daily pressure experienced",
"Daily pressure utilized (partner's view)",
"Daily pushing experienced",
"Daily pushing utilized (partner's view)",
"Day",
"Daily weartime",
# "-- BETWEEN PERSON MAIN EFFECTS",
"Mean persuasion experienced",
"Mean persuasion utilized (partner's view)",
"Mean pressure experienced",
"Mean pressure utilized (partner's view)",
"Mean pushing experienced",
"Mean pushing utilized (partner's view)",
"Mean weartime",
# HURDLE
# "-- WITHIN PERSON MAIN EFFECTS --",
"Hu Daily persuasion experienced",
"Hu Daily persuasion utilized (partner's view)", # OR partner received
"Hu Daily pressure experienced",
"Hu Daily pressure utilized (partner's view)",
"Hu Daily pushing experienced",
"Hu Daily pushing utilized (partner's view)",
"Hu Day",
"Hu Daily weartime",
# "-- BETWEEN PERSON MAIN EFFECTS",
"Hu Mean persuasion experienced",
"Hu Mean persuasion utilized (partner's view)",
"Hu Mean pressure experienced",
"Hu Mean pressure utilized (partner's view)",
"Hu Mean pushing experienced",
"Hu Mean pushing utilized (partner's view)",
"Hu Mean weartime"
)
model_rownames_fixed_hu_ordinal <- c(
model_rownames_fixed_hu[1:2],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rownames_fixed_hu[3:length(model_rownames_fixed_hu)]
)
model_rownames_random_hu <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(Hurdle Intercept)',
"sd(Daily persuasion experienced)",
"sd(Daily persuasion utilized (partner's view))", # OR partner received
"sd(Daily pressure experienced)",
"sd(Daily pressure utilized (partner's view))",
"sd(Daily pushing experienced)",
"sd(Daily pushing utilized (partner's view))",
# Hurdle
"sd(Hu Daily persuasion experienced)",
"sd(Hu Daily persuasion utilized (partner's view))", # OR partner received
"sd(Hu Daily pressure experienced)",
"sd(Hu Daily pressure utilized (partner's view))",
"sd(Hu Daily pushing experienced)",
"sd(Hu Daily pushing utilized (partner's view))",
# '-- CORRELATION STRUCTURE -- ',
'sigma'
)
model_rownames_random_hu_ordinal <- c(model_rownames_random_hu,'disc')rows_to_pack_hu <- list(
"Conditional Within-Person Effects" = c(3,10),
"Conditional Between-Person Effects" = c(11,17),
"Hurdle Within-Person Effects" = c(18,25),
"Hurdle Between-Person Effects" = c(26,32),
"Random Effects" = c(33, 46),
"Additional Parameters" = c(47,47)
)
rows_to_pack_hu_ordinal <- list(
"Intercepts" = c(1,7),
"Conditional Within-Person Effects" = c(3+5,10+5),
"Conditional Between-Person Effects" = c(11+5,17+5),
"Hurdle Within-Person Effects" = c(18+5,25+5),
"Hurdle Between-Person Effects" = c(26+5,32+5),
"Random Effects" = c(33+5, 46+5),
"Additional Parameters" = c(47+5,47+6)
)## [1] 0 720
formula <- bf(
pa_sub ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(1 + persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
hu = ~ persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(1 + persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 2)", class = "b", dpar = "hu")
, brms::set_prior("normal(0, 50)", class = "Intercept") # for non-zero PA
, brms::set_prior("normal(0.5, 2.5)", class = "Intercept", dpar = 'hu') # hurdle part
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = hurdle_lognormal()
#)
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
pa_sub <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::hurdle_lognormal(),
#family = brms::hurdle_negbinomial(),
#family = brms::hurdle_poisson(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 42,
file = file.path("models_cache_brms", paste0("pa_sub_hu_lognormal_NOAR", suffix))
#, file_refit = 'always'
)## Warning: Rows containing NAs were excluded from the model.
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## Computed from 40000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -10490.4 166.4
## p_loo 181.3 6.2
## looic 20980.8 332.8
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 2.0]).
##
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 7, observations = 3736, p-value = 0.8
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.0005353319 0.0032119914
## sample estimates:
## outlier frequency (expected: 0.00166220556745182 )
## 0.001873662
if (do_priorsense) {
priorsense_vars <- c(
'Intercept',
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
)
hurdle_priorsense_vars <- c(
'Intercept_hu',
'b_hu_persuasion_self_cw',
'b_hu_persuasion_partner_cw',
'b_hu_pressure_self_cw',
'b_hu_pressure_partner_cw',
'b_hu_pushing_self_cw',
'b_hu_pushing_partner_cw'
)
gc()
priorsense::powerscale_sensitivity(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
priorsense::powerscale_plot_dens(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
priorsense::powerscale_plot_ecdf(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
priorsense::powerscale_plot_quantities(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
}# summarize with rope range for hurdle part
summary_pa_sub_hurdle <- summarize_brms(
pa_sub,
stats_to_report = c('pd', 'ROPE'),
rope_range = c(-0.18, 0.18),
model_rows_fixed = model_rows_fixed_hu,
model_rows_random = model_rows_random_hu,
model_rownames_fixed = model_rownames_fixed_hu,
model_rownames_random = model_rownames_random_hu,
exponentiate = T) ## Warning in summarize_brms(pa_sub, stats_to_report = c("pd", "ROPE"), rope_range
## = c(-0.18, : Coefficients were exponentiated. Double check if this was
## intended.
# rope range for continuous part of the model
rope_factor <- sd(log(pa_sub$data$pa_sub[pa_sub$data$pa_sub > 0]))
rope_range_continuous = c(-0.1 * rope_factor, 0.1 * rope_factor)
summary_pa_sub_continuous <- summarize_brms(
pa_sub,
rope_range = rope_range_continuous,
model_rows_fixed = model_rows_fixed_hu,
model_rows_random = model_rows_random_hu,
model_rownames_fixed = model_rownames_fixed_hu,
model_rownames_random = model_rownames_random_hu,
exponentiate = T) ## Sampling priors, please wait...
## Warning in summarize_brms(pa_sub, rope_range = rope_range_continuous,
## model_rows_fixed = model_rows_fixed_hu, : Coefficients were exponentiated.
## Double check if this was intended.
# Replace only the ROPE and % in Rope columns for rows with 'Hu'
summary_pa_sub <- summary_pa_sub_continuous
columns_to_replace <- c("ROPE", "inside ROPE")
summary_pa_sub[grepl('Hu', rownames(summary_pa_sub)), columns_to_replace] <-
summary_pa_sub_hurdle[grepl('Hu', rownames(summary_pa_sub_hurdle)), columns_to_replace]
# Print the updated dataframe
summary_pa_sub %>%
print_df(rows_to_pack = rows_to_pack_hu)| exp(Est.) | SE | 95% CI | pd | ROPE | inside ROPE | BF | BF_Evidence | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept | 47.95*** | 3.03 | [42.23, 54.34] | 100.00% | [0.92, 1.08] | 0.000 | >100 | Overwhelming Evidence | 1.001 | 7325 | 14978 |
| Hurdle Intercept | 0.85 | 0.14 | [ 0.61, 1.18] | 83.80% | [0.84, 1.20] | 0.515 | 0.099 | Strong Evidence for Null | 1.000 | 4644 | 10721 |
| Conditional Within-Person Effects | |||||||||||
| Daily persuasion experienced | 1.03 | 0.03 | [ 0.97, 1.08] | 83.24% | [0.92, 1.08] | 0.969 | 0.017 | Very Strong Evidence for Null | 1.000 | 11179 | 19513 |
| Daily persuasion utilized (partner’s view) | 1.03 | 0.02 | [ 0.98, 1.08] | 90.16% | [0.92, 1.08] | 0.976 | 0.023 | Very Strong Evidence for Null | 1.000 | 16280 | 22705 |
| Daily pressure experienced | 0.89* | 0.04 | [ 0.80, 0.98] | 98.71% | [0.92, 1.08] | 0.215 | 0.281 | Moderate Evidence for Null | 1.000 | 25549 | 25127 |
| Daily pressure utilized (partner’s view) | 0.94 | 0.04 | [ 0.86, 1.03] | 91.68% | [0.92, 1.08] | 0.650 | 0.045 | Strong Evidence for Null | 1.000 | 27538 | 22984 |
| Daily pushing experienced | 1.03 | 0.04 | [ 0.96, 1.10] | 76.93% | [0.92, 1.08] | 0.933 | 0.019 | Very Strong Evidence for Null | 1.000 | 22879 | 25906 |
| Daily pushing utilized (partner’s view) | 0.99 | 0.03 | [ 0.93, 1.05] | 63.65% | [0.92, 1.08] | 0.978 | 0.013 | Very Strong Evidence for Null | 1.000 | 22162 | 25742 |
| Day | 1.01 | 0.06 | [ 0.89, 1.14] | 54.95% | [0.92, 1.08] | 0.789 | 0.025 | Very Strong Evidence for Null | 1.000 | 39583 | 31325 |
| Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Conditional Between-Person Effects | |||||||||||
| Mean persuasion experienced | 1.01 | 0.16 | [ 0.74, 1.39] | 53.40% | [0.92, 1.08] | 0.384 | 0.063 | Strong Evidence for Null | 1.000 | 5679 | 11615 |
| Mean persuasion utilized (partner’s view) | 0.98 | 0.15 | [ 0.72, 1.35] | 53.95% | [0.92, 1.08] | 0.384 | 0.064 | Strong Evidence for Null | 1.001 | 5613 | 11737 |
| Mean pressure experienced | 1.14 | 0.21 | [ 0.80, 1.64] | 76.54% | [0.92, 1.08] | 0.263 | 0.093 | Strong Evidence for Null | 1.001 | 8538 | 16974 |
| Mean pressure utilized (partner’s view) | 0.88 | 0.17 | [ 0.61, 1.29] | 74.45% | [0.92, 1.08] | 0.261 | 0.094 | Strong Evidence for Null | 1.000 | 8892 | 17039 |
| Mean pushing experienced | 1.33 | 0.31 | [ 0.84, 2.09] | 88.55% | [0.92, 1.08] | 0.128 | 0.195 | Moderate Evidence for Null | 1.001 | 8468 | 16152 |
| Mean pushing utilized (partner’s view) | 1.40 | 0.33 | [ 0.88, 2.24] | 92.27% | [0.92, 1.08] | 0.094 | 0.265 | Moderate Evidence for Null | 1.001 | 7823 | 16081 |
| Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hurdle Within-Person Effects | |||||||||||
| Hu Daily persuasion experienced | 1.53*** | 0.10 | [ 1.36, 1.75] | 100.00% | [0.84, 1.20] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 18293 | 23098 |
| Hu Daily persuasion utilized (partner’s view) | 1.32*** | 0.08 | [ 1.19, 1.50] | 100.00% | [0.84, 1.20] | 0.038 | >100 | Overwhelming Evidence | 1.000 | 21760 | 23402 |
| Hu Daily pressure experienced | 0.82 | 0.13 | [ 0.58, 1.13] | 89.75% | [0.84, 1.20] | 0.431 | 0.183 | Moderate Evidence for Null | 1.000 | 23948 | 22959 |
| Hu Daily pressure utilized (partner’s view) | 1.47* | 0.27 | [ 1.05, 2.32] | 98.77% | [0.84, 1.20] | 0.115 | 1.184 | Weak Evidence | 1.000 | 22679 | 16886 |
| Hu Daily pushing experienced | 1.71*** | 0.28 | [ 1.27, 2.46] | 99.97% | [0.84, 1.20] | 0.009 | 25.585 | Strong Evidence | 1.000 | 18260 | 21188 |
| Hu Daily pushing utilized (partner’s view) | 1.83*** | 0.23 | [ 1.46, 2.44] | 100.00% | [0.84, 1.20] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 21693 | 23686 |
| Hu Day | 0.92 | 0.12 | [ 0.71, 1.19] | 74.29% | [0.84, 1.20] | 0.739 | 0.081 | Strong Evidence for Null | 1.000 | 42026 | 30353 |
| Hu Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hurdle Between-Person Effects | |||||||||||
| Hu Mean persuasion experienced | 1.19 | 0.46 | [ 0.54, 2.60] | 67.33% | [0.84, 1.20] | 0.321 | 0.217 | Moderate Evidence for Null | 1.001 | 4572 | 9486 |
| Hu Mean persuasion utilized (partner’s view) | 1.18 | 0.46 | [ 0.53, 2.56] | 66.74% | [0.84, 1.20] | 0.327 | 0.213 | Moderate Evidence for Null | 1.001 | 4614 | 9826 |
| Hu Mean pressure experienced | 0.31** | 0.13 | [ 0.13, 0.74] | 99.63% | [0.84, 1.20] | 0.011 | 7.828 | Moderate Evidence | 1.000 | 7034 | 15087 |
| Hu Mean pressure utilized (partner’s view) | 0.57 | 0.25 | [ 0.24, 1.36] | 90.16% | [0.84, 1.20] | 0.143 | 0.504 | Weak Evidence for Null | 1.000 | 7037 | 15000 |
| Hu Mean pushing experienced | 2.70 | 1.53 | [ 0.87, 8.16] | 95.77% | [0.84, 1.20] | 0.056 | 1.372 | Weak Evidence | 1.000 | 7198 | 13867 |
| Hu Mean pushing utilized (partner’s view) | 2.79 | 1.57 | [ 0.89, 8.46] | 96.16% | [0.84, 1.20] | 0.051 | 1.415 | Weak Evidence | 1.000 | 6943 | 12974 |
| Hu Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | |||||||||||
| sd(Intercept) | 0.32 | 0.04 | [0.24, 0.42] | NA | NA | NA | NA | NA | 1.000 | 8803 | 15643 |
| sd(Hurdle Intercept) | 0.89 | 0.12 | [0.69, 1.18] | NA | NA | NA | NA | NA | 1.000 | 7673 | 14307 |
| sd(Daily persuasion experienced) | 0.12 | 0.02 | [0.08, 0.17] | NA | NA | NA | NA | NA | 1.000 | 16113 | 24856 |
| sd(Daily persuasion utilized (partner’s view)) | 0.09 | 0.02 | [0.05, 0.13] | NA | NA | NA | NA | NA | 1.000 | 19267 | 21079 |
| sd(Daily pressure experienced) | 0.07 | 0.06 | [0.00, 0.24] | NA | NA | NA | NA | NA | 1.001 | 12569 | 14433 |
| sd(Daily pressure utilized (partner’s view)) | 0.06 | 0.05 | [0.00, 0.18] | NA | NA | NA | NA | NA | 1.000 | 15590 | 14458 |
| sd(Daily pushing experienced) | 0.11 | 0.04 | [0.04, 0.19] | NA | NA | NA | NA | NA | 1.000 | 11320 | 7606 |
| sd(Daily pushing utilized (partner’s view)) | 0.09 | 0.03 | [0.02, 0.17] | NA | NA | NA | NA | NA | 1.000 | 10989 | 6835 |
| sd(Hu Daily persuasion experienced) | 0.18 | 0.08 | [0.02, 0.34] | NA | NA | NA | NA | NA | 1.000 | 8938 | 6082 |
| sd(Hu Daily persuasion utilized (partner’s view)) | 0.17 | 0.08 | [0.02, 0.33] | NA | NA | NA | NA | NA | 1.000 | 9475 | 7735 |
| sd(Hu Daily pressure experienced) | 0.25 | 0.21 | [0.01, 0.85] | NA | NA | NA | NA | NA | 1.001 | 11940 | 15619 |
| sd(Hu Daily pressure utilized (partner’s view)) | 0.28 | 0.25 | [0.01, 1.00] | NA | NA | NA | NA | NA | 1.000 | 10937 | 15282 |
| sd(Hu Daily pushing experienced) | 0.62 | 0.18 | [0.32, 1.08] | NA | NA | NA | NA | NA | 1.000 | 15379 | 20817 |
| sd(Hu Daily pushing utilized (partner’s view)) | 0.31 | 0.15 | [0.04, 0.64] | NA | NA | NA | NA | NA | 1.000 | 10681 | 7340 |
| Additional Parameters | |||||||||||
| sigma | 0.68 | 0.01 | [0.66, 0.71] | NA | NA | NA | NA | NA | 1.000 | 38393 | 29400 |
# Plot continuous part of model
variable <- c(
'(Intercept)',
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
)
plot(
bayestestR::p_direction(pa_sub, parameter = variable),
priors = TRUE
) + theme_bw()plot(
bayestestR::rope(
pa_sub,
parameter = variable,
range = rope_range_continuous,
verbose = F,
ci = 1
)
) + theme_bw()# Hurdle part of the model
variable <- c(
'b_hu_persuasion_self_cw',
'b_hu_persuasion_partner_cw',
'b_hu_pressure_self_cw',
'b_hu_pressure_partner_cw',
'b_hu_pushing_self_cw',
'b_hu_pushing_partner_cw'
)
plot(
bayestestR::p_direction(pa_sub, parameter = variable),
priors = TRUE
) + theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
# The rope range for the bernoulli part of the model is -0.18, 0.18
plot(
bayestestR::rope(pa_sub, parameter = variable, range = c(-0.18, 0.18), ci = 1),
verbose = FALSE
) + theme_bw()## Possible multicollinearity between b_persuasion_partner_cb and
## b_persuasion_self_cb (r = 0.77), b_hu_persuasion_partner_cb and
## b_hu_persuasion_self_cb (r = 0.83). This might lead to inappropriate
## results. See 'Details' in '?rope'.
conds_eff <- conditional_spaghetti(
pa_sub,
effects = c(
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw'
),
x_label = c(
'Received Persuasion',
'Exerted Persuasion',
'Received Pressure',
'Exerted Pressure',
'Received Plan-Related Pushing',
'Exerted Plan-Related Pushing'
),
group_var = 'coupleID',
plot_full_range = TRUE,
y_limits = c(0, 100),
y_label = "Same-Day MVPA",
y_labels = c('Probability of Being Active', 'Minutes of MVPA When Active', 'Overall Expected Minutes of MVPA'),
, filter_quantiles = .9995
, font_family = 'Candara'
)## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
$persuasion_self_cw
## Warning: Removed 158 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 158 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.00654
$persuasion_partner_cw
## Warning: Removed 137 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 129 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.00537
$pressure_self_cw
## Warning: Removed 45 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 51 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.00939
$pressure_partner_cw
## Warning: Removed 45 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 87 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.0156
$pushing_self_cw
## Warning: Removed 35 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 145 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.0153
$pushing_partner_cw
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 63 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.0125
Note. This graphic illustrates the relationship between
social control and moderate to vigorous physical activity (MVPA) using a
Bayesian Hurdle-Lognormal Multilevel Model. The predictor is centered
within individuals to examine how deviations from their average social
control relate to same-day MVPA. Shaded areas indicate credible
intervals, thick lines show fixed effects, and thin lines represent
random effects, highlighting variability across couples. The plots
display the probability of being active, expected minutes of MVPA when
active, and combined predicted MVPA. The bottom density plot visualizes
the posterior distributions of slope estimates, transformed to represent
multiplicative changes in odds ratios (hurdle component) or expected
values. Medians and 95% credible intervals (2.5th and 97.5th
percentiles) are shown. Effects are significant, when the 95% credible
interval does not overlap 1.
x_label = c(
'Received Persuasion',
'Exerted Persuasion',
'Received Pressure',
'Exerted Pressure',
'Received Plan-Related Pushing',
'Exerted Plan-Related Pushing'
)
for (i in 1:length(conds_eff)) {
effname <- names(conds_eff)[i]
eff_plot <- conds_eff[[i]]
x_label_i <- x_label[[i]]
rmarkdown::render(
"C:/Users/kueng/DataAnalysis/02TimeAndTiesControl/Output/Plots/BeautifulPlotWithNote.Rmd",
output_file = paste0('C:/Users/kueng/DataAnalysis/02TimeAndTiesControl/Output/Plots/Graphic_', effname, '.pdf'),
params = list(
p_i = eff_plot,
p_name = effname,
x_label = x_label_i
),
envir = new.env(),
quiet = TRUE
)
}##
## Estimate 2.5 % 97.5 %
## 30.9 29.4 32.5
##
## Type: response
## Columns: estimate, conf.low, conf.high
## [1] 5.75 971.25
formula <- bf(
pa_obj ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day + weartime_self_cw + weartime_self_cb +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 50)", class = "Intercept")
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = lognormal()
# )
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
pa_obj_log <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = lognormal(),
#control = list(adapt_delta = 0.95),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("pa_obj_log_gaussian_NOAR", suffix))
)## Warning: Rows containing NAs were excluded from the model.
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## Computed from 40000 by 3337 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -18781.8 68.4
## p_loo 92.1 4.5
## looic 37563.7 136.8
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 2.3]).
##
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
## DHARMa:testOutliers with type = binomial may have inflated Type I error rates for integer-valued distributions. To get a more exact result, it is recommended to re-run testOutliers with type = 'bootstrap'. See ?testOutliers for details
##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 24, observations = 3337, p-value < 2.2e-16
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.001341025 0.004652382
## sample estimates:
## outlier frequency (expected: 0.00287383877734492 )
## 0.007192089
if (do_priorsense) {
gc()
priorsense::powerscale_sensitivity(pa_obj_log, variable = priorsense_vars)
priorsense::powerscale_plot_dens(pa_obj_log, variable = priorsense_vars)
priorsense::powerscale_plot_ecdf(pa_obj_log, variable = priorsense_vars)
priorsense::powerscale_plot_quantities(pa_obj_log, variable = priorsense_vars)
}# rope range for lognormal model
rope_factor <- sd(log(pa_obj_log$data$pa_obj))
rope_range_log = c(-0.1 * rope_factor, 0.1 * rope_factor)
summarize_brms(
pa_obj_log,
rope_range = rope_range_log,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)## Sampling priors, please wait...
## Warning in summarize_brms(pa_obj_log, rope_range = rope_range_log,
## model_rows_fixed = model_rows_fixed, : Coefficients were exponentiated. Double
## check if this was intended.
| exp(Est.) | SE | 95% CI | pd | ROPE | inside ROPE | BF | BF_Evidence | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept | 117.27*** | 6.24 | [105.41, 130.66] | 100.00% | [0.94, 1.07] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 5582 | 10902 |
| Within-Person Effects | |||||||||||
| Daily persuasion experienced | 1.03 | 0.02 | [ 1.00, 1.06] | 96.53% | [0.94, 1.07] | 0.990 | 0.033 | Strong Evidence for Null | 1.000 | 22787 | 26727 |
| Daily persuasion utilized (partner’s view) | 1.02 | 0.02 | [ 0.99, 1.05] | 88.83% | [0.94, 1.07] | 0.997 | 0.014 | Very Strong Evidence for Null | 1.000 | 28722 | 29910 |
| Daily pressure experienced | 0.94 | 0.03 | [ 0.88, 1.01] | 95.99% | [0.94, 1.07] | 0.547 | 0.066 | Strong Evidence for Null | 1.000 | 41819 | 28808 |
| Daily pressure utilized (partner’s view) | 0.98 | 0.03 | [ 0.92, 1.05] | 71.66% | [0.94, 1.07] | 0.905 | 0.016 | Very Strong Evidence for Null | 1.000 | 46892 | 31087 |
| Daily pushing experienced | 1.03 | 0.03 | [ 0.98, 1.08] | 90.00% | [0.94, 1.07] | 0.907 | 0.025 | Very Strong Evidence for Null | 1.000 | 32795 | 27546 |
| Daily pushing utilized (partner’s view) | 1.02 | 0.02 | [ 0.97, 1.06] | 77.40% | [0.94, 1.07] | 0.986 | 0.011 | Very Strong Evidence for Null | 1.000 | 46123 | 31056 |
| Day | 0.97 | 0.03 | [ 0.91, 1.04] | 78.37% | [0.94, 1.07] | 0.849 | 0.019 | Very Strong Evidence for Null | 1.000 | 63974 | 30299 |
| Daily weartime | 1.00*** | 0.00 | [ 1.00, 1.00] | 100.00% | [0.94, 1.07] | 1.000 | 0.106 | Moderate Evidence for Null | 1.000 | 41200 | 26262 |
| Between-Person Effects | |||||||||||
| Mean persuasion experienced | 1.10 | 0.16 | [ 0.83, 1.47] | 75.59% | [0.94, 1.07] | 0.275 | 0.074 | Strong Evidence for Null | 1.001 | 5262 | 10215 |
| Mean persuasion utilized (partner’s view) | 0.98 | 0.14 | [ 0.73, 1.31] | 55.00% | [0.94, 1.07] | 0.349 | 0.057 | Strong Evidence for Null | 1.001 | 5260 | 10280 |
| Mean pressure experienced | 0.98 | 0.14 | [ 0.73, 1.31] | 56.39% | [0.94, 1.07] | 0.339 | 0.059 | Strong Evidence for Null | 1.001 | 7201 | 14024 |
| Mean pressure utilized (partner’s view) | 0.96 | 0.14 | [ 0.72, 1.28] | 60.24% | [0.94, 1.07] | 0.339 | 0.059 | Strong Evidence for Null | 1.001 | 6726 | 13627 |
| Mean pushing experienced | 0.97 | 0.20 | [ 0.64, 1.47] | 56.15% | [0.94, 1.07] | 0.242 | 0.083 | Strong Evidence for Null | 1.000 | 7856 | 13134 |
| Mean pushing utilized (partner’s view) | 1.24 | 0.25 | [ 0.83, 1.88] | 85.86% | [0.94, 1.07] | 0.141 | 0.147 | Moderate Evidence for Null | 1.000 | 7770 | 12879 |
| Mean weartime | 1.00 | 0.00 | [ 1.00, 1.00] | 91.16% | [0.94, 1.07] | 1.000 | 0.000 | Very Strong Evidence for Null | 1.000 | 50538 | 35353 |
| Random Effects | |||||||||||
| sd(Intercept) | 0.30 | 0.04 | [0.24, 0.40] | NA | NA | NA | NA | NA | 1.000 | 7902 | 14886 |
| sd(Daily persuasion experienced) | 0.05 | 0.01 | [0.02, 0.08] | NA | NA | NA | NA | NA | 1.000 | 20801 | 17210 |
| sd(Daily persuasion utilized (partner’s view)) | 0.06 | 0.02 | [0.03, 0.09] | NA | NA | NA | NA | NA | 1.000 | 20659 | 19450 |
| sd(Daily pressure experienced) | 0.04 | 0.03 | [0.00, 0.13] | NA | NA | NA | NA | NA | 1.000 | 17044 | 16600 |
| sd(Daily pressure utilized (partner’s view)) | 0.04 | 0.03 | [0.00, 0.11] | NA | NA | NA | NA | NA | 1.000 | 23038 | 19677 |
| sd(Daily pushing experienced) | 0.08 | 0.04 | [0.01, 0.15] | NA | NA | NA | NA | NA | 1.000 | 8952 | 10098 |
| sd(Daily pushing utilized (partner’s view)) | 0.04 | 0.03 | [0.00, 0.10] | NA | NA | NA | NA | NA | 1.000 | 13943 | 18163 |
| Additional Parameters | |||||||||||
| sigma | 0.57 | 0.01 | [0.56, 0.59] | NA | NA | NA | NA | NA | 1.000 | 60296 | 29184 |
plot(
bayestestR::p_direction(pa_obj_log),
priors = TRUE
) +
coord_cartesian(xlim = c(-3, 3)) +
theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
## Possible multicollinearity between b_persuasion_partner_cb and
## b_persuasion_self_cb (r = 0.89), b_pressure_self_cb and
## b_persuasion_self_cb (r = 0.74), b_pressure_partner_cb and
## b_persuasion_self_cb (r = 0.74), b_pressure_self_cb and
## b_persuasion_partner_cb (r = 0.72), b_pressure_partner_cb and
## b_persuasion_partner_cb (r = 0.78), b_pushing_partner_cb and
## b_pushing_self_cb (r = 0.83). This might lead to inappropriate results.
## See 'Details' in '?rope'.
## [1] 0 5
formula <- bf(
aff ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 5)", class = "b")
,brms::set_prior("normal(0, 20)", class = "Intercept", lb=1, ub=6)
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = gaussian()
# )
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
mood_gauss <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("mood_gauss_NOAR", suffix))
)## Warning: Rows containing NAs were excluded from the model.
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Found 1 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## Computed from 40000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -5186.2 59.2
## p_loo 75.4 3.3
## looic 10372.5 118.4
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 2.2]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 3735 100.0% 5780
## (0.7, 1] (bad) 1 0.0% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: model.check
## outliers at both margin(s) = 27, observations = 3736, p-value =
## 2.533e-08
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
## 0.004767871 0.010497581
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 )
## 0.007226981
if (do_priorsense) {
gc()
priorsense::powerscale_sensitivity(mood_gauss, variable = priorsense_vars)
priorsense::powerscale_plot_dens(mood_gauss, variable = priorsense_vars)
priorsense::powerscale_plot_ecdf(mood_gauss, variable = priorsense_vars)
priorsense::powerscale_plot_quantities(mood_gauss, variable = priorsense_vars)
}summarize_brms(
mood_gauss,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = F) %>%
print_df(rows_to_pack = rows_to_pack)## Sampling priors, please wait...
| Est. | SE | 95% CI | pd | ROPE | inside ROPE | BF | BF_Evidence | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept | 3.70*** | 0.10 | [ 3.50, 3.91] | 100.00% | [-0.11, 0.11] | 0.000 | >100 | Overwhelming Evidence | 1.002 | 4495 | 9545 |
| Within-Person Effects | |||||||||||
| Daily persuasion experienced | 0.00 | 0.02 | [-0.04, 0.05] | 54.54% | [-0.11, 0.11] | 1.000 | 0.004 | Very Strong Evidence for Null | 1.000 | 35462 | 27914 |
| Daily persuasion utilized (partner’s view) | 0.02 | 0.02 | [-0.02, 0.07] | 82.80% | [-0.11, 0.11] | 1.000 | 0.007 | Very Strong Evidence for Null | 1.000 | 29416 | 30178 |
| Daily pressure experienced | -0.04 | 0.05 | [-0.15, 0.07] | 76.90% | [-0.11, 0.11] | 0.919 | 0.013 | Very Strong Evidence for Null | 1.000 | 35865 | 27332 |
| Daily pressure utilized (partner’s view) | -0.02 | 0.05 | [-0.14, 0.08] | 66.80% | [-0.11, 0.11] | 0.934 | 0.012 | Very Strong Evidence for Null | 1.000 | 33073 | 24399 |
| Daily pushing experienced | 0.02 | 0.03 | [-0.04, 0.08] | 75.78% | [-0.11, 0.11] | 0.997 | 0.008 | Very Strong Evidence for Null | 1.000 | 41974 | 29904 |
| Daily pushing utilized (partner’s view) | 0.08* | 0.03 | [ 0.01, 0.14] | 98.45% | [-0.11, 0.11] | 0.867 | 0.080 | Strong Evidence for Null | 1.000 | 31271 | 28932 |
| Day | 0.26*** | 0.06 | [ 0.15, 0.37] | 100.00% | [-0.11, 0.11] | 0.005 | >100 | Overwhelming Evidence | 1.000 | 59723 | 29576 |
| Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | |||||||||||
| Mean persuasion experienced | 0.35 | 0.28 | [-0.20, 0.89] | 89.30% | [-0.11, 0.11] | 0.152 | 0.123 | Moderate Evidence for Null | 1.000 | 4262 | 9105 |
| Mean persuasion utilized (partner’s view) | 0.23 | 0.28 | [-0.31, 0.78] | 79.91% | [-0.11, 0.11] | 0.227 | 0.080 | Strong Evidence for Null | 1.000 | 4267 | 8956 |
| Mean pressure experienced | -0.32 | 0.27 | [-0.86, 0.23] | 87.62% | [-0.11, 0.11] | 0.170 | 0.108 | Moderate Evidence for Null | 1.000 | 4747 | 11106 |
| Mean pressure utilized (partner’s view) | -0.32 | 0.28 | [-0.86, 0.23] | 87.67% | [-0.11, 0.11] | 0.173 | 0.110 | Moderate Evidence for Null | 1.000 | 4951 | 11430 |
| Mean pushing experienced | 0.22 | 0.38 | [-0.55, 1.01] | 71.39% | [-0.11, 0.11] | 0.200 | 0.089 | Strong Evidence for Null | 1.001 | 6400 | 11111 |
| Mean pushing utilized (partner’s view) | 0.35 | 0.38 | [-0.41, 1.14] | 82.26% | [-0.11, 0.11] | 0.156 | 0.118 | Moderate Evidence for Null | 1.001 | 6362 | 11744 |
| Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | |||||||||||
| sd(Intercept) | 0.60 | 0.08 | [0.48, 0.78] | NA | NA | NA | NA | NA | 1.001 | 7483 | 13998 |
| sd(Daily persuasion experienced) | 0.04 | 0.03 | [0.00, 0.10] | NA | NA | NA | NA | NA | 1.001 | 10758 | 15861 |
| sd(Daily persuasion utilized (partner’s view)) | 0.08 | 0.03 | [0.02, 0.13] | NA | NA | NA | NA | NA | 1.000 | 11761 | 9894 |
| sd(Daily pressure experienced) | 0.07 | 0.06 | [0.00, 0.24] | NA | NA | NA | NA | NA | 1.000 | 16544 | 18107 |
| sd(Daily pressure utilized (partner’s view)) | 0.08 | 0.07 | [0.00, 0.26] | NA | NA | NA | NA | NA | 1.000 | 14798 | 17566 |
| sd(Daily pushing experienced) | 0.05 | 0.04 | [0.00, 0.14] | NA | NA | NA | NA | NA | 1.000 | 13961 | 15345 |
| sd(Daily pushing utilized (partner’s view)) | 0.07 | 0.05 | [0.00, 0.17] | NA | NA | NA | NA | NA | 1.000 | 13129 | 14428 |
| Additional Parameters | |||||||||||
| sigma | 0.96 | 0.01 | [0.94, 0.98] | NA | NA | NA | NA | NA | 1.000 | 63319 | 29501 |
plot(
bayestestR::p_direction(mood_gauss),
priors = TRUE
) +
coord_cartesian(xlim = c(-3, 3)) +
theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
## Possible multicollinearity between b_pressure_self_cb and
## b_persuasion_self_cb (r = 0.82), b_pressure_partner_cb and
## b_persuasion_self_cb (r = 0.8), b_pressure_self_cb and
## b_persuasion_partner_cb (r = 0.8), b_pressure_partner_cb and
## b_persuasion_partner_cb (r = 0.82), b_pressure_partner_cb and
## b_pressure_self_cb (r = 0.77), b_pushing_partner_cb and
## b_pushing_self_cb (r = 0.89). This might lead to inappropriate results.
## See 'Details' in '?rope'.
conditional_spaghetti(
mood_gauss,
effects = c('pushing_partner_cw'),
group_var = 'coupleID',
plot_full_range = TRUE
)$pushing_partner_cw
##
## Estimate 2.5 % 97.5 %
## 3.82 3.79 3.85
##
## Type: response
## Columns: estimate, conf.low, conf.high
## [1] 0 5
df_double$reactance_ordinal <- factor(df_double$reactance,
levels = 0:5,
ordered = TRUE)
formula <- bf(
reactance_ordinal ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = cumulative() # HURDLE_CUMULATIVE
# )
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
reactance_ordinal <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::cumulative(),
#control = list(adapt_delta = 0.95),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777
, file = file.path("models_cache_brms", paste0("reactance_ordinal_NOARNOAR_", suffix))
)## Warning: Rows containing NAs were excluded from the model.
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Found 5 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## Computed from 40000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -682.1 31.9
## p_loo 73.7 5.4
## looic 1364.3 63.9
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 1.6]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 751 99.3% 903
## (0.7, 1] (bad) 5 0.7% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 2, observations = 756, p-value = 0.04
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.000000000 0.001322751
## sample estimates:
## outlier frequency (expected: 0.000436507936507937 )
## 0.002645503
if (do_priorsense) {
gc()
priorsense::powerscale_sensitivity(reactance_ordinal, variable = priorsense_vars)
priorsense::powerscale_plot_dens(reactance_ordinal, variable = priorsense_vars)
priorsense::powerscale_plot_ecdf(reactance_ordinal, variable = priorsense_vars)
priorsense::powerscale_plot_quantities(reactance_ordinal, variable = priorsense_vars)
}summarize_brms(
reactance_ordinal,
rope_range = c(-0.18, 0.18),
model_rows_fixed = model_rows_fixed_ordinal,
model_rows_random = model_rows_random_ordinal,
model_rownames_fixed = model_rownames_fixed_ordinal,
model_rownames_random = model_rownames_random_ordinal,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack_ordinal)## Sampling priors, please wait...
| OR | SE | 95% CI | pd | ROPE | inside ROPE | BF | BF_Evidence | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercepts | |||||||||||
| Intercept | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Intercept[1] | 3.84*** | 0.98 | [ 2.34, 6.49] | 100.00% | [0.84, 1.20] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 28792 | 29901 |
| Intercept[2] | 8.32*** | 2.24 | [ 4.96, 14.45] | 100.00% | [0.84, 1.20] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 29900 | 30020 |
| Intercept[3] | 23.17*** | 6.80 | [ 13.17, 42.42] | 100.00% | [0.84, 1.20] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 31532 | 31530 |
| Intercept[4] | 101.19*** | 35.37 | [ 52.40, 208.53] | 100.00% | [0.84, 1.20] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 34784 | 31711 |
| Intercept[5] | 3422.14*** | 2190.68 | [1091.76, 13601.17] | 100.00% | [0.84, 1.20] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 47511 | 31296 |
| Within-Person Effects | |||||||||||
| Daily persuasion experienced | 0.85* | 0.07 | [ 0.71, 0.99] | 97.97% | [0.84, 1.20] | 0.568 | 0.277 | Moderate Evidence for Null | 1.000 | 33454 | 27707 |
| Daily persuasion utilized (partner’s view) | 1.03 | 0.10 | [ 0.84, 1.24] | 60.38% | [0.84, 1.20] | 0.922 | 0.040 | Strong Evidence for Null | 1.000 | 30268 | 27064 |
| Daily pressure experienced | 1.86* | 0.36 | [ 1.18, 2.69] | 99.38% | [0.84, 1.20] | 0.027 | 3.012 | Moderate Evidence | 1.000 | 19636 | 23573 |
| Daily pressure utilized (partner’s view) | 1.23 | 0.29 | [ 0.70, 2.08] | 80.82% | [0.84, 1.20] | 0.385 | 0.149 | Moderate Evidence for Null | 1.000 | 25256 | 21310 |
| Daily pushing experienced | 1.16 | 0.11 | [ 0.96, 1.42] | 94.52% | [0.84, 1.20] | 0.618 | 0.136 | Moderate Evidence for Null | 1.000 | 28471 | 27169 |
| Daily pushing utilized (partner’s view) | 0.92 | 0.11 | [ 0.71, 1.17] | 76.80% | [0.84, 1.20] | 0.758 | 0.062 | Strong Evidence for Null | 1.000 | 32368 | 25496 |
| Day | 1.47 | 0.51 | [ 0.77, 2.86] | 87.16% | [0.84, 1.20] | 0.227 | 0.260 | Moderate Evidence for Null | 1.000 | 45161 | 31117 |
| Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | |||||||||||
| Mean persuasion experienced | 1.12 | 0.57 | [ 0.41, 3.14] | 59.12% | [0.84, 1.20] | 0.268 | 0.212 | Moderate Evidence for Null | 1.000 | 13321 | 21398 |
| Mean persuasion utilized (partner’s view) | 1.38 | 0.79 | [ 0.45, 4.35] | 71.27% | [0.84, 1.20] | 0.216 | 0.263 | Moderate Evidence for Null | 1.000 | 14141 | 21745 |
| Mean pressure experienced | 3.48* | 1.90 | [ 1.18, 10.66] | 98.83% | [0.84, 1.20] | 0.022 | 3.043 | Moderate Evidence | 1.000 | 15342 | 23058 |
| Mean pressure utilized (partner’s view) | 1.18 | 0.67 | [ 0.37, 3.58] | 61.23% | [0.84, 1.20] | 0.236 | 0.241 | Moderate Evidence for Null | 1.000 | 15692 | 22489 |
| Mean pushing experienced | 1.20 | 0.89 | [ 0.28, 5.59] | 59.92% | [0.84, 1.20] | 0.189 | 0.305 | Weak Evidence for Null | 1.000 | 18260 | 25276 |
| Mean pushing utilized (partner’s view) | 0.11* | 0.10 | [ 0.02, 0.64] | 99.30% | [0.84, 1.20] | 0.008 | 8.595 | Moderate Evidence | 1.000 | 23596 | 28667 |
| Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | |||||||||||
| sd(Intercept) | 0.81 | 0.20 | [0.48, 1.26] | NA | NA | NA | NA | NA | 1.000 | 14049 | 23078 |
| sd(Daily persuasion experienced) | 0.16 | 0.12 | [0.01, 0.42] | NA | NA | NA | NA | NA | 1.000 | 7067 | 14443 |
| sd(Daily persuasion utilized (partner’s view)) | 0.21 | 0.14 | [0.01, 0.51] | NA | NA | NA | NA | NA | 1.000 | 10643 | 14404 |
| sd(Daily pressure experienced) | 0.55 | 0.25 | [0.10, 1.14] | NA | NA | NA | NA | NA | 1.001 | 9501 | 8812 |
| sd(Daily pressure utilized (partner’s view)) | 0.40 | 0.38 | [0.02, 1.55] | NA | NA | NA | NA | NA | 1.000 | 10032 | 16731 |
| sd(Daily pushing experienced) | 0.20 | 0.13 | [0.01, 0.51] | NA | NA | NA | NA | NA | 1.000 | 10833 | 13309 |
| sd(Daily pushing utilized (partner’s view)) | 0.14 | 0.13 | [0.01, 0.59] | NA | NA | NA | NA | NA | 1.000 | 15145 | 18957 |
| Additional Parameters | |||||||||||
| sigma | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| disc | 1.00 | 0.00 | [1.00, 1.00] | NA | NA | NA | NA | NA | NA | NA | NA |
plot(
bayestestR::p_direction(reactance_ordinal),
priors = TRUE
) +
coord_cartesian(xlim = c(-6, 6)) +
theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
## Possible multicollinearity between b_Intercept[4] and b_Intercept[2] (r
## = 0.76), b_Intercept[4] and b_Intercept[3] (r = 0.83),
## b_pressure_self_cb and b_persuasion_self_cb (r = 0.72),
## b_pressure_partner_cb and b_persuasion_partner_cb (r = 0.79). This might
## lead to inappropriate results. See 'Details' in '?rope'.
conditional_spaghetti(
reactance_ordinal,
effects = c('persuasion_self_cw', 'pressure_self_cw')
, group_var = 'coupleID'
#, n_groups = 15
, plot_full_range = T
)\(persuasion_self_cw
<img
src="01_FinalModels_files/figure-html/report_reactance_ordinal-3.png"
width="2400" />\)pressure_self_cw
##
## Group Estimate 2.5 % 97.5 %
## 0 0.68193 0.65457 0.7076
## 1 0.09412 0.07508 0.1157
## 2 0.08361 0.06612 0.1041
## 3 0.06984 0.05464 0.0874
## 4 0.06395 0.05130 0.0775
## 5 0.00527 0.00171 0.0115
##
## Type: response
## Columns: group, estimate, conf.low, conf.high
introduce_binary_reactance <- function(data) {
data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
return(data)
}
df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
for (i in seq_along(implist)) {
implist[[i]] <- introduce_binary_reactance(implist[[i]])
}
}
formula <- bf(
is_reactance ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 10)", class = "Intercept", lb=0, ub=5)
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = bernoulli()
# )
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
is_reactance <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::bernoulli(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("is_reactance_NOARNOAR_", suffix))
#, file_refit = 'always'
)## Warning: Rows containing NAs were excluded from the model.
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Found 34 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## Computed from 40000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -363.6 16.0
## p_loo 80.3 6.0
## looic 727.2 32.0
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 1.5]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 722 95.5% 476
## (0.7, 1] (bad) 33 4.4% <NA>
## (1, Inf) (very bad) 1 0.1% <NA>
## See help('pareto-k-diagnostic') for details.
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: model.check
## outliers at both margin(s) = 1, observations = 756, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
## 0.0000334886 0.0073476538
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 )
## 0.001322751
if (do_priorsense) {
gc()
priorsense::powerscale_sensitivity(is_reactance, variable = priorsense_vars)
priorsense::powerscale_plot_dens(is_reactance, variable = priorsense_vars)
priorsense::powerscale_plot_ecdf(is_reactance, variable = priorsense_vars)
priorsense::powerscale_plot_quantities(is_reactance, variable = priorsense_vars)
}summarize_brms(
is_reactance,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)## Sampling priors, please wait...
| OR | SE | 95% CI | pd | ROPE | inside ROPE | BF | BF_Evidence | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept | 0.29*** | 0.08 | [0.16, 0.50] | 100.00% | [0.83, 1.20] | 0.000 | >100 | Overwhelming Evidence | 1.000 | 43132 | 34475 |
| Within-Person Effects | |||||||||||
| Daily persuasion experienced | 0.84 | 0.08 | [0.69, 1.01] | 96.47% | [0.83, 1.20] | 0.552 | 0.191 | Moderate Evidence for Null | 1.000 | 42037 | 30341 |
| Daily persuasion utilized (partner’s view) | 1.12 | 0.16 | [0.85, 1.53] | 78.62% | [0.83, 1.20] | 0.665 | 0.076 | Strong Evidence for Null | 1.000 | 34121 | 28236 |
| Daily pressure experienced | 1.99* | 0.63 | [1.03, 4.47] | 97.91% | [0.83, 1.20] | 0.050 | 1.446 | Weak Evidence | 1.000 | 28128 | 23067 |
| Daily pressure utilized (partner’s view) | 1.41 | 0.59 | [0.59, 4.18] | 80.24% | [0.83, 1.20] | 0.241 | 0.239 | Moderate Evidence for Null | 1.000 | 27770 | 24147 |
| Daily pushing experienced | 1.27* | 0.15 | [1.01, 1.63] | 97.89% | [0.83, 1.20] | 0.309 | 0.391 | Weak Evidence for Null | 1.000 | 45536 | 30516 |
| Daily pushing utilized (partner’s view) | 0.89 | 0.17 | [0.60, 1.31] | 73.50% | [0.83, 1.20] | 0.570 | 0.093 | Strong Evidence for Null | 1.000 | 43340 | 30983 |
| Day | 1.64 | 0.63 | [0.76, 3.51] | 89.82% | [0.83, 1.20] | 0.165 | 0.350 | Weak Evidence for Null | 1.000 | 66504 | 30884 |
| Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | |||||||||||
| Mean persuasion experienced | 1.99 | 1.20 | [0.60, 6.84] | 87.26% | [0.83, 1.20] | 0.127 | 0.462 | Weak Evidence for Null | 1.000 | 22054 | 28793 |
| Mean persuasion utilized (partner’s view) | 1.88 | 1.25 | [0.52, 7.23] | 82.88% | [0.83, 1.20] | 0.137 | 0.419 | Weak Evidence for Null | 1.000 | 23180 | 27564 |
| Mean pressure experienced | 17.42** | 18.55 | [2.39, 155.22] | 99.77% | [0.83, 1.20] | 0.003 | 22.823 | Strong Evidence | 1.000 | 30159 | 28196 |
| Mean pressure utilized (partner’s view) | 2.32 | 2.55 | [0.25, 19.47] | 77.24% | [0.83, 1.20] | 0.099 | 0.593 | Weak Evidence for Null | 1.000 | 26009 | 28694 |
| Mean pushing experienced | 0.82 | 0.81 | [0.12, 6.10] | 58.04% | [0.83, 1.20] | 0.144 | 0.408 | Weak Evidence for Null | 1.000 | 28811 | 29362 |
| Mean pushing utilized (partner’s view) | 0.08* | 0.08 | [0.01, 0.65] | 99.01% | [0.83, 1.20] | 0.009 | 7.113 | Moderate Evidence | 1.000 | 32814 | 31176 |
| Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | |||||||||||
| sd(Intercept) | 1.15 | 0.25 | [0.74, 1.74] | NA | NA | NA | NA | NA | 1.000 | 17091 | 25690 |
| sd(Daily persuasion experienced) | 0.20 | 0.14 | [0.01, 0.51] | NA | NA | NA | NA | NA | 1.000 | 9903 | 17543 |
| sd(Daily persuasion utilized (partner’s view)) | 0.48 | 0.20 | [0.12, 0.97] | NA | NA | NA | NA | NA | 1.000 | 14070 | 15231 |
| sd(Daily pressure experienced) | 1.06 | 0.55 | [0.14, 2.41] | NA | NA | NA | NA | NA | 1.000 | 9656 | 11046 |
| sd(Daily pressure utilized (partner’s view)) | 0.81 | 0.67 | [0.04, 2.73] | NA | NA | NA | NA | NA | 1.000 | 14835 | 20412 |
| sd(Daily pushing experienced) | 0.23 | 0.16 | [0.01, 0.59] | NA | NA | NA | NA | NA | 1.000 | 14895 | 17689 |
| sd(Daily pushing utilized (partner’s view)) | 0.25 | 0.22 | [0.01, 0.93] | NA | NA | NA | NA | NA | 1.001 | 17388 | 22176 |
| Additional Parameters | |||||||||||
| sigma | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot(
bayestestR::p_direction(is_reactance),
priors = TRUE
) +
coord_cartesian(xlim = c(-6, 6)) +
theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
conditional_spaghetti(
is_reactance,
effects = c('pressure_self_cw', 'pushing_self_cw'),
group_var = 'coupleID',
plot_full_range = TRUE
)\(pressure_self_cw
<img
src="01_FinalModels_files/figure-html/report_is_reactance-3.png"
width="2400" />\)pushing_self_cw
##
## Estimate 2.5 % 97.5 %
## 0.329 0.304 0.355
##
## Type: response
## Columns: estimate, conf.low, conf.high
## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (pressure_self_cw... > 0 0.47 0.39 -0.15 1.13 8.81
## Post.Prob Star
## 1 0.9
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.
process_model_component <- function(obj) {
# Convert to string, modify, and evaluate
name <- deparse(substitute(obj))
if (report_hurdle) name <- paste0(name, '_hu')
if (report_ordinal) name <- paste0(name, '_ordinal')
return(get(name, envir = parent.frame()))
}
all_models <- report_side_by_side(
pa_sub,
pa_obj_log,
mood_gauss,
reactance_ordinal,
is_reactance,
stats_to_report = c('CI', 'pd'),
model_rows_random = process_model_component(model_rows_random),
model_rows_fixed = process_model_component(model_rows_fixed),
model_rownames_random = process_model_component(model_rownames_random),
model_rownames_fixed = process_model_component(model_rownames_fixed)
) [1] “pa_sub”
## Warning in summarize_brms(model, exponentiate = exponentiate, stats_to_report =
## stats_to_report, : Coefficients were exponentiated. Double check if this was
## intended.
[1] “pa_obj_log”
## Warning in summarize_brms(model, exponentiate = exponentiate, stats_to_report =
## stats_to_report, : Coefficients were exponentiated. Double check if this was
## intended.
[1] “mood_gauss” [1] “reactance_ordinal” [1] “is_reactance”
# pretty printing
summary_all_models <- all_models %>%
print_df(rows_to_pack = process_model_component(rows_to_pack)) %>%
add_header_above(
c(" ", "Subjective MVPA Hurdle Lognormal" = (length(all_models) / 5),
"Device-Based MVPA Log (Gaussian)" = (length(all_models) / 5),
"Mood Gaussian" = (length(all_models) / 5),
"Reactance Ordinal" = (length(all_models) / 5),
"Reactance Dichotome" = (length(all_models) / 5))
)
export_xlsx(
summary_all_models,
rows_to_pack = process_model_component(rows_to_pack),
file.path("Output", "AllModelsFinal.xlsx"),
merge_option = 'header',
simplify_2nd_row = TRUE,
line_above_rows = c(1,2),
line_below_rows = c(-1)
)
print(summary_all_models)| exp(Est.) pa_sub | 95% CI pa_sub | pd pa_sub | exp(Est.) pa_obj_log | 95% CI pa_obj_log | pd pa_obj_log | Est. mood_gauss | 95% CI mood_gauss | pd mood_gauss | OR reactance_ordinal | 95% CI reactance_ordinal | pd reactance_ordinal | OR is_reactance | 95% CI is_reactance | pd is_reactance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept | 47.95*** | [42.23, 54.34] | 100.00% | 117.27*** | [105.41, 130.66] | 100.00% | 3.70*** | [ 3.50, 3.91] | 100.00% | NA | NA | NA | 0.29*** | [0.16, 0.50] | 100.00% |
| Hurdle Intercept | 0.85 | [ 0.61, 1.18] | 83.80% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Conditional Within-Person Effects | |||||||||||||||
| Daily persuasion experienced | 1.03 | [ 0.97, 1.08] | 83.24% | 1.03 | [ 1.00, 1.06] | 96.53% | 0.00 | [-0.04, 0.05] | 54.54% | 0.85* | [ 0.71, 0.99] | 97.97% | 0.84 | [0.69, 1.01] | 96.47% |
| Daily persuasion utilized (partner’s view) | 1.03 | [ 0.98, 1.08] | 90.16% | 1.02 | [ 0.99, 1.05] | 88.83% | 0.02 | [-0.02, 0.07] | 82.80% | 1.03 | [ 0.84, 1.24] | 60.38% | 1.12 | [0.85, 1.53] | 78.62% |
| Daily pressure experienced | 0.89* | [ 0.80, 0.98] | 98.71% | 0.94 | [ 0.88, 1.01] | 95.99% | -0.04 | [-0.15, 0.07] | 76.90% | 1.86* | [ 1.18, 2.69] | 99.38% | 1.99* | [1.03, 4.47] | 97.91% |
| Daily pressure utilized (partner’s view) | 0.94 | [ 0.86, 1.03] | 91.68% | 0.98 | [ 0.92, 1.05] | 71.66% | -0.02 | [-0.14, 0.08] | 66.80% | 1.23 | [ 0.70, 2.08] | 80.82% | 1.41 | [0.59, 4.18] | 80.24% |
| Daily pushing experienced | 1.03 | [ 0.96, 1.10] | 76.93% | 1.03 | [ 0.98, 1.08] | 90.00% | 0.02 | [-0.04, 0.08] | 75.78% | 1.16 | [ 0.96, 1.42] | 94.52% | 1.27* | [1.01, 1.63] | 97.89% |
| Daily pushing utilized (partner’s view) | 0.99 | [ 0.93, 1.05] | 63.65% | 1.02 | [ 0.97, 1.06] | 77.40% | 0.08* | [ 0.01, 0.14] | 98.45% | 0.92 | [ 0.71, 1.17] | 76.80% | 0.89 | [0.60, 1.31] | 73.50% |
| Day | 1.01 | [ 0.89, 1.14] | 54.95% | 0.97 | [ 0.91, 1.04] | 78.37% | 0.26*** | [ 0.15, 0.37] | 100.00% | 1.47 | [ 0.77, 2.86] | 87.16% | 1.64 | [0.76, 3.51] | 89.82% |
| Daily weartime | NA | NA | NA | 1.00*** | [ 1.00, 1.00] | 100.00% | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Conditional Between-Person Effects | |||||||||||||||
| Mean persuasion experienced | 1.01 | [ 0.74, 1.39] | 53.40% | 1.10 | [ 0.83, 1.47] | 75.59% | 0.35 | [-0.20, 0.89] | 89.30% | 1.12 | [ 0.41, 3.14] | 59.12% | 1.99 | [0.60, 6.84] | 87.26% |
| Mean persuasion utilized (partner’s view) | 0.98 | [ 0.72, 1.35] | 53.95% | 0.98 | [ 0.73, 1.31] | 55.00% | 0.23 | [-0.31, 0.78] | 79.91% | 1.38 | [ 0.45, 4.35] | 71.27% | 1.88 | [0.52, 7.23] | 82.88% |
| Mean pressure experienced | 1.14 | [ 0.80, 1.64] | 76.54% | 0.98 | [ 0.73, 1.31] | 56.39% | -0.32 | [-0.86, 0.23] | 87.62% | 3.48* | [ 1.18, 10.66] | 98.83% | 17.42** | [2.39, 155.22] | 99.77% |
| Mean pressure utilized (partner’s view) | 0.88 | [ 0.61, 1.29] | 74.45% | 0.96 | [ 0.72, 1.28] | 60.24% | -0.32 | [-0.86, 0.23] | 87.67% | 1.18 | [ 0.37, 3.58] | 61.23% | 2.32 | [0.25, 19.47] | 77.24% |
| Mean pushing experienced | 1.33 | [ 0.84, 2.09] | 88.55% | 0.97 | [ 0.64, 1.47] | 56.15% | 0.22 | [-0.55, 1.01] | 71.39% | 1.20 | [ 0.28, 5.59] | 59.92% | 0.82 | [0.12, 6.10] | 58.04% |
| Mean pushing utilized (partner’s view) | 1.40 | [ 0.88, 2.24] | 92.27% | 1.24 | [ 0.83, 1.88] | 85.86% | 0.35 | [-0.41, 1.14] | 82.26% | 0.11* | [ 0.02, 0.64] | 99.30% | 0.08* | [0.01, 0.65] | 99.01% |
| Mean weartime | NA | NA | NA | 1.00 | [ 1.00, 1.00] | 91.16% | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hurdle Within-Person Effects | |||||||||||||||
| Hu Daily persuasion experienced | 1.53*** | [ 1.36, 1.75] | 100.00% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Daily persuasion utilized (partner’s view) | 1.32*** | [ 1.19, 1.50] | 100.00% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Daily pressure experienced | 0.82 | [ 0.58, 1.13] | 89.75% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Daily pressure utilized (partner’s view) | 1.47* | [ 1.05, 2.32] | 98.77% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Daily pushing experienced | 1.71*** | [ 1.27, 2.46] | 99.97% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Daily pushing utilized (partner’s view) | 1.83*** | [ 1.46, 2.44] | 100.00% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Day | 0.92 | [ 0.71, 1.19] | 74.29% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hurdle Between-Person Effects | |||||||||||||||
| Hu Mean persuasion experienced | 1.19 | [ 0.54, 2.60] | 67.33% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Mean persuasion utilized (partner’s view) | 1.18 | [ 0.53, 2.56] | 66.74% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Mean pressure experienced | 0.31** | [ 0.13, 0.74] | 99.63% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Mean pressure utilized (partner’s view) | 0.57 | [ 0.24, 1.36] | 90.16% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Mean pushing experienced | 2.70 | [ 0.87, 8.16] | 95.77% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Mean pushing utilized (partner’s view) | 2.79 | [ 0.89, 8.46] | 96.16% | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Hu Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | |||||||||||||||
| sd(Intercept) | 0.32 | [0.24, 0.42] | NA | 0.30 | [0.24, 0.40] | NA | 0.60 | [0.48, 0.78] | NA | 0.81 | [0.48, 1.26] | NA | 1.15 | [0.74, 1.74] | NA |
| sd(Hurdle Intercept) | 0.89 | [0.69, 1.18] | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| sd(Daily persuasion experienced) | 0.12 | [0.08, 0.17] | NA | 0.05 | [0.02, 0.08] | NA | 0.04 | [0.00, 0.10] | NA | 0.16 | [0.01, 0.42] | NA | 0.20 | [0.01, 0.51] | NA |
| sd(Daily persuasion utilized (partner’s view)) | 0.09 | [0.05, 0.13] | NA | 0.06 | [0.03, 0.09] | NA | 0.08 | [0.02, 0.13] | NA | 0.21 | [0.01, 0.51] | NA | 0.48 | [0.12, 0.97] | NA |
| sd(Daily pressure experienced) | 0.07 | [0.00, 0.24] | NA | 0.04 | [0.00, 0.13] | NA | 0.07 | [0.00, 0.24] | NA | 0.55 | [0.10, 1.14] | NA | 1.06 | [0.14, 2.41] | NA |
| sd(Daily pressure utilized (partner’s view)) | 0.06 | [0.00, 0.18] | NA | 0.04 | [0.00, 0.11] | NA | 0.08 | [0.00, 0.26] | NA | 0.40 | [0.02, 1.55] | NA | 0.81 | [0.04, 2.73] | NA |
| sd(Daily pushing experienced) | 0.11 | [0.04, 0.19] | NA | 0.08 | [0.01, 0.15] | NA | 0.05 | [0.00, 0.14] | NA | 0.20 | [0.01, 0.51] | NA | 0.23 | [0.01, 0.59] | NA |
| sd(Daily pushing utilized (partner’s view)) | 0.09 | [0.02, 0.17] | NA | 0.04 | [0.00, 0.10] | NA | 0.07 | [0.00, 0.17] | NA | 0.14 | [0.01, 0.59] | NA | 0.25 | [0.01, 0.93] | NA |
| sd(Hu Daily persuasion experienced) | 0.18 | [0.02, 0.34] | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| sd(Hu Daily persuasion utilized (partner’s view)) | 0.17 | [0.02, 0.33] | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| sd(Hu Daily pressure experienced) | 0.25 | [0.01, 0.85] | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| sd(Hu Daily pressure utilized (partner’s view)) | 0.28 | [0.01, 1.00] | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| sd(Hu Daily pushing experienced) | 0.62 | [0.32, 1.08] | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| sd(Hu Daily pushing utilized (partner’s view)) | 0.31 | [0.04, 0.64] | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Additional Parameters | |||||||||||||||
| sigma | 0.68 | [0.66, 0.71] | NA | 0.57 | [0.56, 0.59] | NA | 0.96 | [0.94, 0.98] | NA | NA | NA | NA | NA | NA | NA |
Analyses were conducted using the R Statistical language (version 4.4.1; R Core Team, 2024) on Windows 11 x64 (build 22635)